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Original citation: Fenske, James and Kala, Namrata. (2015) Climate and the slave trade. Journal of Development Economics, 112. pp. 19-32. Permanent WRAP URL: http://wrap.warwick.ac.uk/85606 Copyright and reuse: The Warwick Research Archive Portal (WRAP) makes this work by researchers of the University of Warwick available open access under the following conditions. Copyright © and all moral rights to the version of the paper presented here belong to the individual author(s) and/or other copyright owners. To the extent reasonable and practicable the material made available in WRAP has been checked for eligibility before being made available. Copies of full items can be used for personal research or study, educational, or not-for-profit purposes without prior permission or charge. Provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way. Publisher’s statement: © 2015, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/

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CLIMATE AND THE SLAVE TRADE

JAMES FENSKE† AND NAMRATA KALA?

ABSTRACT. African societies exported more slaves in colder years. Lower temperaturesreduced mortality and raised agricultural yields, lowering slave supply costs. Our resultshelp explain African participation in the slave trade, which predicts adverse outcomestoday. We use an annual panel of African temperatures and port-level slave exports toshow that exports declined when local temperatures were warmer than normal. Thisresult is strongest where African ecosystems are least resilient to climate change. Coldweather shocks at the peak of the slave trade predict lower economic activity today. Wesupport our interpretation using the histories of Whydah, Benguela, and Mozambique.

1. INTRODUCTION

One of the key mechanisms through which geography affects modern developmentis its influence on historical events. Many of the deep roots of economic developmenthave been shaped by the environment, including the timing of the adoption of agri-culture (Ashraf and Michalopoulos, 2014), ethnic diversity (Michalopoulos, 2012), andthe nature of ethnic institutions (Alsan, 2014). As Nunn (2014) points out, “[t]he im-pacts of geography depend crucially on the particular historical context.” The intersec-tions of the disease environment with the process of European colonization (Acemogluet al., 2001), droughts with the course of the Mexican revolution (Dell, 2012), and terrainruggedness with Africa’s slave trades (Nunn and Puga, 2012) are all examples of howthe environment has shaped present-day outcomes because it has mattered at specific

†UNIVERSITY OF OXFORD?YALE UNIVERSITY

E-mail addresses: [email protected], [email protected]: October 1, 2014.This paper was previously titled “Climate, ecosystem resilience, and the slave trade.” We are grateful toAchyuta Adhvaryu, Gareth Austin, Sonia Bhalotra, Mike Boozer, Rahul Deb, Kenneth Gillingham, Tim-othy Guinnane, Anke Hoeffler, Remi Jedwab, Ruixue Jia, Christopher Ksoll, Robert Mendelsohn, SteliosMichalopoulos, Philip Morgan, Nathan Nunn, Anant Nyshadham, Ayodeji Olukoju, Andrew Plantinga,Florian Ploeckl, Carol Propper, Simon Smith, Christopher Udry, Nicolas Van de Sijpe, Warren Whatley, andthe participants of seminars at the ASSA Annual Meetings, the BETA Workshop in Historical Economics,the University of Bristol, Brown University, the Centre for the Study of African Economies Annual Con-ference, the Centre for Economic Policy Research Economic History Symposium, the Economic HistoryAssociation Annual Meeting, George Washington University, the Royal Economic Society Annual Con-ference, Tilburg University, Yale University, the World Congress of Cliometrics, and the World EconomicHistory Congress for their comments.

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2 JAMES FENSKE AND NAMRATA KALA

moments in the past. In this paper, we show that environmental shocks shaped the dy-namics of the transatlantic slave trade. Through the persistent effects of this trade, thesepast weather conditions continue to influence outcomes in the present.

Our approach is to use reconstructed annual data on African temperatures to mea-sure the year-to-year variation in weather conditions over space during the time of thetransatlantic slave trade. We use this data to construct port-specific annual temperatureshocks, and combine these with port-level annual slave exports. The panel nature of thisdata allows us to control both for port-level heterogeneity and for the flexible evolutionof the slave trade as a whole over time. We find a considerable decrease in the numberof slaves shipped from ports in warmer years. This result is robust to several alternativespecifications, including aggregated units of observation, addition of port-specific timetrends, and estimation on sub-samples partitioned over time and space. In addition tostudying annual temperatures, we also examine the role of longer-term environmentalfactors by looking at the effect of climate (that is, long-run trends in temperature) onslave exports, and find effects that are the same in sign and much larger in magnitude.1

Our interpretation is that warmer temperatures led to increased costs of raiding forslaves. Higher temperatures reduce productivity in tropical agriculture (Kurukulasuriyaand Mendelsohn, 2008; Lobell and Field, 2007; Tan and Shibasaki, 2003) and increasemortality (Burgess et al., 2011). In our baseline specification, the decline in slave exportsin response to a 1◦C temperature increase is roughly 3,000 slaves. This is comparable tothe mean of all non-zero port-year observations of slave exports. A one-standard devi-ation increase in temperature relative to the port mean is a smaller shock, equivalentto 0.16◦C; the decline in exports in response to this shock is roughly 500 slaves, and iscomparable to the mean number of slaves exported across all port-year observations,including zeroes.

We argue that this effect worked through higher costs of collecting taxes and tributefor local states, lower productivity in supporting sectors of the economy, greater disor-der in the regions where slaves were usually captured, and higher slave mortality. Weshow that the effect we find is stronger in Africa’s sub-humid and dry savannah regionsthan it is in areas of moist savannah and humid forest. That is, the regions of Africain which agricultural productivity is most sensitive to fluctuations in temperature (Seoet al., 2009) were those that responded most in terms of slave exports. Further, we findthat both long-run trends in climate and short-run shocks around these trends havepower to explain variation in slave exports. We support our interpretation using casestudies of three ports that are influential in our results: Benguela, Whydah, and Mozam-bique. Our results confirm the importance of supply-side environmental factors in ac-counting for the trans-Atlantic slave trade.

1Climate science usually distinguishes between short-run “weather” and long-run “climate.” Climate isa statistical description, usually the mean and variability, of relevant quantities over a period of time. Asdefined by the World Meteorological Organization, this time period is 30 years (IPCC, 2007).

CLIMATE AND THE SLAVE TRADE 3

Using modern-day light density at night to proxy for economic activity, we find thatthe regions around ports that received cold temperature shocks at the peak of the trans-Atlantic slave trade are poorer today. Over the long run, then, the negative impacts ofgreater participation in the slave trade outweigh the transitory benefits of greater pro-ductivity and reduced mortality. The literature on the long run effects of the slave tradehas emphasized several channels through which this effect may operate. Many of theseare consistent with the data; we show that the incidence of conflict is lower, levels oftrust are greater, local and traditional authorities are more responsive, and women’s out-comes improve in regions that experienced warm temperatures during the slave trade

1.1. Contribution. Our results help explain the relationship between the environmentand development. A large literature has emphasized the role of geographic characteris-tics in shaping economic outcomes in the present, in particular through their impact oninstitutions (Acemoglu et al., 2012a, 2001). Our results relate past environmental shocksin Africa to its present poverty through the adverse long-run effects of the slave trade.

The unchanging nature of geographic endowments makes it difficult to separate theirdirect and indirect effects from the impacts of local unobservable variables. Recentwork, then, has used natural experiments such as the eradication of endemic diseases(Bleakley, 2007) or variation over time in temperature and rainfall (Bruckner and Cic-cone, 2011; Dell et al., 2012). Abrupt and persistent changes in climate have precipitatedeconomic collapse through lowered agricultural productivity, depopulation, the declineof cities and the weakening of states (Chaney, 2013; DeMenocal et al., 2001; Diamond,2005; Haug et al., 2003; Hornbeck, 2012; Weiss and Bradley, 2001). The mechanisms forthese effects are not yet fully understood. We give evidence that the impact of tempera-ture shocks on sectors outside of agriculture has not been confined to the industrial era,and we provide one possible mechanism by which temperature shocks affect modernincomes. We show that even small, short-run changes had large impacts on the pro-ductive sectors and coping mechanisms of African societies. The slave trade’s effects onmodern-day institutions, mistrust and poverty in Africa are, then, partly reflections ofthe continent’s environmental history.

We also add to existing knowledge of the economics of conflict. To the extent that cur-rent economic growth attenuates the rise of conflict (Collier and Hoeffler, 2004), we con-tribute to the literature that explains how history matters for modern conflict. Strongcorrelations between economic shocks, economic grievances, and the onset of con-flict have been discussed in the literature (Bruckner and Ciccone, 2010; Ciccone, 2011;Miguel et al., 2004). The proposed mechanisms for this link focus on the greater rela-tive returns and lower costs of insurrection during periods of reduced income (Blattmanand Miguel, 2010; Chassang and Padro-i Miquel, 2009, 2010).

It is not established that the same relationships have held in the past, nor has it beenshown whether endemic, parasitic violence will respond in the same way to economic


shocks. Violence in Colombia intensifies when coca or oil prices rise (Angrist and Ku-gler, 2008; Dube and Vargas, 2013), livestock raiding in Kenya intensifies when herdsare healthy (Witsenburg and Adano, 2009), and Japan’s long recession has cut into theyakuza’s profits from racketeering (Hill, 2006, p. 247). The dynamics of the slave trade,then, followed a logic similar to the model of Besley and Persson (2011); greater staterevenues encouraged repression (slave raiding) under non-cohesive political institu-tions. The slave trade is, then, relevant to the broader literature on the roles of institu-tions and resources in precipitating and perpetuating conflict (Acemoglu et al., 2012b,2010; Mehlum et al., 2006).

We also contribute to a literature on the economics of the slave trade. The trade inten-sified internal slavery and lawlessness, and distorted economic and political institutions(Acemoglu and Robinson, 2010). Today, regions that exported more slaves have lowerincomes (Nunn, 2008), lower levels of human capital (Obikili, 2013a), are less trusting(Nunn and Wantchekon, 2011), and are more ethnically divided (Whatley and Gillezeau,2011). Despite the importance of the slave trade, little is known about the influence ofAfrican factors on the supply of slaves. Whatley’s (2008) paper on the guns-for-slavescycle is the only empirical study of African supply dynamics of which we are aware.

A more narrow literature in African history has discussed the role of environmen-tal shocks in the slave trade. Historians such as Hartwig (1979) have suggested thatdroughts and famines may have either increased or decreased the supply of slaves.Lovejoy (2000, p.29,71) argues, for example, that droughts pushed Africans into areasthat had been previously depopulated by the slave trade. These individuals then fell vic-tim to slaving once normal conditions resumed. Miller (1982), by contrast, argues thatthat people, particularly children, were sold into slavery in west-central Africa in orderto survive crop failure. For Searing (1993, p.81,83), local food shortages encouraged localslaveowners to sell their slaves to Europeans, but also increased slave mortality. Desertmerchants could shut Atlantic merchants out of local grain markets during periods offamine. Crises pushed people to sell themselves or their dependants into slavery, butalso led to death and dispersion that reduced the availability of slaves for export andthe provisions needed to feed them. Lacking consistent data over time and space, theselocal qualitative studies have been unable to find the net effect of environmental stresson slave supply. We provide the first such estimates.

We proceed as follows. In section 2, we outline our empirical approach and describeour sources of data on temperature shocks and slave exports. In section 3, we provideour baseline results and demonstrate their statistical robustness. We show that the ef-fect of temperature differs by agro-ecological zone. We decompose the effect of temper-ature into long-run trends and fluctuations around it. In section 4, we show the impactof past temperature shocks on modern light density and discuss possible mechanismsfor this persistence. In section 5, we explain the results. We provide a simple model and


argument to account for greater slave exports during years of better agricultural pro-ductivity and lower mortality. We discuss evidence from the secondary literature thatconnects warmer temperatures to increased mortality and reduced agricultural produc-tivity. We support our interpretation by examining case studies of three important slaveports – Whydah, Benguela, and Mozambique. Section 6 concludes.

2. EMPIRICAL STRATEGY AND DATA

2.1. Empirical strategy. Our data will consist of a panel of annual slave exports andtemperatures for 134 ports that were engaged in the trans-Atlantic slave trade. Thedependent variable of interest, the number of slaves exported from port i in year t, isbounded below by 0. Thus, our main specification is the following:

(1) slavesi,t = max(0, α+ β temperaturei,t + δi + ηt + εi,t)

Here, slavesi,t is number of slaves exported from port i in year t. temperaturei,t is thetemperature at port i in year t, δi is a port-level fixed effect, ηt is a year fixed effect and εitis the error term. We estimate (1) using a tobit estimator.2 We use ports as the unit of ob-servation because this is the finest geographical level at which the data on slave exportsare available; we show in section 3.3 that we can find similar results using alternativeunits of observation.

Standard errors are clustered by the nearest grid point in our temperature data, sincethere are fewer grid points than there are ports. To further address serial correlation overtime or space, we also report standard errors clustered by year, by unique port locations,by 1◦ × 1◦ squares, and by 2◦ × 2◦ squares. In the online appendix (Table A0), we showthat if we use ordinary least squares to estimate (1), the standard errors do not grow ap-preciably as these squares are made larger, using conventional or Cameron et al. (2008)standard errors. They are also similar in the linear model when Cameron et al. (2008)standard errors are used to cluster by both 1◦ × 1◦ square and year at once.

In addition to using temperature as the key explanatory variable of interest, we alsoestimate the impacts of the long-run moving average (climate) and the variation of tem-perature around this average (climate shocks) on the supply of slaves.

2.2. Data.

2.2.1. Temperature. In order to estimate (1), we use three principal sources of data. Thefirst covers temperature. The historical data are reported as temperature “anomalies,”and are taken from Mann et al. (1998a,b). They reconstruct annual temperature anom-alies using multivariate calibration on a 5◦ by 5◦ grid. They combine data from several

2In a linear fixed effects model, the impact of annual temperature shocks and that of annual temperaturewould be identical, since the long-term mean temperatures would be collinear with the port fixed effect.While this is not true in the case of a tobit estimator, the magnitude of the impact of temperature shocksand temperature are nearly identical.


previous paleoclimatic studies that calculated historical temperatures using data fromdifferent proxy indicators. These include coral, ice cores, tree rings, and other long in-strumental records. The availability of multiple indicators increases the robustness ofthe estimates, and their calculations account for the appropriate potential limitationsof each proxy indicator. They calibrate the proxy dataset using monthly instrumentaldata from 1920-1995, and compute annual temperature anomalies for each year from1730 to 1900 relative to the baseline average temperature during the period 1902 to 1980.A more detailed overview of the data is presented in online appendix C, and additionaldetails of their methodology are available in Mann et al. (1998a,b). The dataset has beenused by numerous climate scientists to study long-term climate warming trends (Coveyet al., 2003; Crowley, 2000; Huang et al., 2000).

A temperature anomaly of 1◦C at port i in year t means that the temperature at i was1◦C higher during t than the mean temperature at i over the period 1902-1980. We re-construct the baseline temperatures for each port using a separate temperature seriesfrom the University of Delaware, which covers the 1902-1980 period. This permits us toconvert the anomalies into an annual temperature series for each port.3 There is con-siderable variation in temperature across years for each port, and shocks within a singleyear vary across ports. In the online appendix, we present the time series of tempera-ture shocks for two of our case studies: Benguela and Whydah. In more than 30% of theyears in our data, one of these ports is experiencing a shock above its long-run meanwhile the other is experiencing the opposite.

In addition to using these temperatures directly, we convert them into fluctuationsaround longer-run climate trends by removing the 30-year running mean from eachport. These are then treated as shocks over and above the long-term trend in climate.In our analysis, we also use this running mean of climate as a regressor to estimate theimpact of changes in longer-run climate on the dynamics of the slave trade. Where dataare missing on the 5◦ by 5◦ grid, we impute anomalies separately for each year usinga cubic polynomial in latitude and longitude, with full interactions. Because our dataare annual, we are unable to isolate temperature shocks during critical months in theagricultural calendar. This attenuation bias will push our results towards zero.

We have used the temperature series from Mann et al. (1998a), rather than the up-dated series from Mann et al. (2009) for our analysis. This is because the revised se-ries is smoothed and is highly persistent over time for individual ports. This creates anunrealistic absence of year-to-year variation in temperature and introduces substantialserial correlation. Although temperature residuals after port and year fixed effects are

3Baseline temperatures can be downloaded from http://climate.geog.udel.edu/~climate/html_

pages/download.html#P2009. We originally downloaded the historical anomalies from http://picasso.

ngdc.noaa.gov/paleo/data/mann/. These have since been moved to http://www.ncdc.noaa.gov/

paleo/pubs/mann1998/frames.htm, and we are willing to provide the data on request. Vlassopoulos et al.(2009) have used these data previously.


removed from the Mann et al. (1998a) and Mann et al. (2009) series are positively cor-related, the partial R-squared of this correlation is less than 0.01. As a result, the Mannet al. (2009) data provide similar coefficient estimates but much larger standard errorsthan the Mann et al. (1998a) data in our main results, results by agro-ecological zone,and results for climate trends. For example, our baseline estimate of β in (1) is -3,052using the Mann et al. (1998a) and -2,594 using the Mann et al. (2009) revisions. How-ever, the standard error rises from 1,115 to 2,463.

2.2.2. Slave exports. The second source of data that we use is the Trans-Atlantic SlaveTrade Database of Eltis et al. (1999).4 The trans-Atlantic slave trade, which is the focus ofthis study, comprised roughly two thirds of the volume of slaves transported from Africabetween 1400 and 1900 (Nunn, 2008). Because the temperature data are only availableafter 1730, we are confined to analyzing the impact on the slave trade during this period.Since the overwhelming bulk of slaves were shipped across the Atlantic in this period,we are able to study the slave trade when it was at its most active. The database providesvoyage-level data on more than 34,000 voyages, including information on the numberof slaves carried, the year the ship departed Africa, and the principal port of slave pur-chase, which is the port where the largest number of captives embarked.

We convert these raw data into an annual port-level panel. Since not all ships em-barked from known ports or, in some cases, known regions, this requires assigning sev-eral of the slaves to ports. 60% of slaves come from ports with known latitude-longitudecoordinates. 20% come from a known region (such as the Bight of Benin) but with noport given in the raw data. 20% come from voyages in which only the year is known.5

We assign slaves from ships from known regions and unknown ports in proportion tothe number of slaves that are exported from the known ports within that region in agiven year. Analogously, we assign slaves from ships from unknown regions and un-known ports in proportion to the number of slaves that are exported from all knownports within a given year. We obtain a panel of 134 ports spanning 137 years, from 1730to 1866.

Temperature shocks for each port are computed by taking the four nearest points inthe temperature data and interpolating bilinearly.6 We treat these as proxies for condi-tions within the catchment zone of each port, since the vast majority of slaves camefrom areas within 100 miles of the coast (Evans and Richardson, 1995, p. 675), eventhough slaving frontiers did expand inland over time (Miller, 1996). Similarly, the esti-mates in Nunn and Wantchekon (2011) suggest that roughly 90% of slave exports camefrom ethnic groups with centroids within 500km of the coast (see Figure A.1 in onlineappendix D).

4The database is online, at http://www.slavevoyages.org.5Fewer than 1% of slaves in the data come from ports to which we have been unable to assign geographiccoordinates. We treat these ports as observations with a known region, but no known port.6There is no noticeable difference in the magnitude or variance of temperature shocks for the points overwater relative to the points over land, and so we do not treat them differently.


We map both the temperature points for which Mann et al. (1998a) report their dataand the ports reported in the Trans-Atlantic Slave Trade Database in Figure 1. Summarystatistics for our sample are given in Table 1. A kernel density plot of slave exports isincluded in online appendix F. The mean number of slaves exported annually per portis close to 450, and increases to roughly 2,500 when we only consider ports that exporteda non-zero number of slaves in a given year. The standard deviation reported in thetable conflates differences in temperatures across ports with within-port variation. Thestandard deviation of temperature with port means removed is roughly 0.16◦C.

2.2.3. Agro-ecological zones. The third source of data is on agro-ecological zones (AEZs).These data classify land into zones based on climate, elevation, soils and latitude, andare compiled by the Food and Agriculture Organization (FAO). The original AEZ classi-fication classifies land in Africa into 16 zones, which includes five climatic zones eachat three levels of elevation (high, medium and low), and the desert. These AEZs are sta-ble across time, since they are classified using factors such as long-term climate, soil,elevation and latitude. To estimate the effects of temperature separately by AEZ, wecollapse the same ecological zone at each elevation into a single classification. For in-stance, we classify high-elevation dry savannah, mid-elevation dry savannah and low-elevation dry savannah all as “dry savannah”. Ports are assigned the AEZ of the near-est African administrative unit in the data used by Kala et al. (2012). The 134 ports inour data comprise desert, dry savannah, moist savannah, sub-humid, and humid forestzones.

3. RESULTS

3.1. Main results. We present our main results in Table 2. We find that a one degree in-crease in temperature leads to a one-year drop of roughly 3,000 slaves from the treatedport. This is a sizeable effect, roughly equal to the mean for a port whose exports arenonzero in a given year. For a one standard deviation increase in de-meaned tempera-ture (roughly 0.16◦C), the effect would be about 480 slaves.7 This is roughly a one quarterof a standard deviation movement in slave exports.

Scientific evidence indicates that the process of multi-proxy historical temperaturereconstruction may create a temperature series with dampened variability (Christiansenand Ljungqvist, 2011; Riedwyl et al., 2009; von Storch et al., 2004). This dampeningwould scale up our estimated coefficient. In the baseline period 1902-80, the port-specific temperature anomalies have a standard deviation of 0.42◦C. If our historicaltemperature data have been dampened by the ratio 0.16/0.42, then our coefficient esti-mates should be re-scaled by this same ratio. This gives a slave supply response to a 1◦C

7This is smaller than the standard deviation reported in Table 1, since that figure reflects variations intemperature across ports, rather than fluctuations experienced by individual ports over time.


FIGURE 1. Map of ports and temperature points

The black circles are the ports that appear in the Trans-Atlantic Slave Trade Database. The greysquares are the points of the 5◦ by 5◦ grid on which Mann et al. (1998a) record temperature anom-alies.

temperature shock of roughly 1,200 fewer slaves. This is approximately a two thirds of astandard deviation reduction in slave exports.8

Though this magnitude may appear large, a one-degree higher temperature over anentire year is a significant shock. Dell et al. (2012) show that a one degree tempera-ture increase in the present day is associated with lower economic growth by about 1.3

8While it is possible that temperature has non-linear impacts on slave exports, the linear relationship isa good approximation of this effect. One of our primary proposed mechanisms is the link between tem-perature and agricultural productivity, discussed in section 3.2. Studies of this relationship in Africa findsmall and often insignificant effects of higher-order polynomial terms in temperature (Kurukulasuriyaet al., 2011; Kurukulasuriya and Mendelsohn, 2008). Further, studies linking temperature to economicoutcomes generally rely on linear specifications (Burgess et al., 2011; Dell et al., 2012). If we include aquadratic term for temperature, we find that the marginal effect is smaller at greater temperatures, di-minishing from roughly−5, 000 at 20◦C to roughly−2, 600 at 25◦C (not reported). Interacting temperatureshocks with mean temperature shows a similar pattern: the effect of a 1◦ shock is weaker at warmer ports(not reported).


percentage points in poorer countries, and impacts both the agricultural and industrialsectors.

3.2. Mechanisms.

FIGURE 2. Map of agro-ecological zones

Point data on agro-ecological zones are taken from Kala et al. (2012) and converted to polygons byconstructing Thiessen polygons around each point.

3.2.1. Results by ecological zone, crop, and region. In Table 3, we show the results differacross African agro-ecological zones (AEZs). We map these AEZs in Figure 2. The gen-eral pattern that emerges is that the elasticity of slave exports with respect to temper-ature is greater in drier environments. These results suggest that agricultural produc-tivity was an important channel, since these are the regions in which agriculture wouldbe most sensitive to fluctuations in weather (Seo et al., 2009). The largest impact is ondry savannah and deserts followed by sub-humid zones, and the lowest impacts are onmoist savannah and humid forest. p-values for the equality of coefficients across AEZsare presented in the online appendix, in Table A3. We also interact temperature with


an indicator for humidity above the median in this table.9 The interaction is significant;above-median humidity completely attenuates the effect of temperature.

What drives these interactions? Kala et al. (2012) analyze current agricultural produc-tivity by AEZ, and find that moist savannah and sub-humid zones, where the impacts oftemperature on slave exports are relatively minor, are more productive in general thandry savannah zones. At high and mid-elevations, sub-humid zones can have produc-tivity similar to (or even greater than) that of moist savannahs. This helps explain whyboth have intermediate coefficients between the large impact on dry savannah and thenegligible impact on humid forest. Other analyses of ecological zones in Africa find thatthe growing season is longer in sub-humid and humid zones than in semi-arid and aridzones (Bationo et al., 1998). Plant growth potential is also higher in sub-humid and hu-mid areas (Ojwang et al., 2010). Both tendencies make these areas less vulnerable toshocks.

Higher temperatures are more likely to exacerbate the disease burdens of malaria andtrypanosomiasis in humid regions (Munang’andu et al., 2012; Ye et al., 2007), but we seelarger impacts of higher temperatures in dry ecological zones. Thus, while the hetero-geneous effects of temperature across AEZs are unlikely to be explained by its impacton the disease burden, it may increase mortality by operating through the agriculturalchannel. That is, since warmer years are years of lower agricultural productivity, theymay also be years of higher mortality due to food scarcity. Thus, agricultural produc-tivity could directly affect the costs of raiding as we discuss below, or indirectly affect itthrough the channel of increased mortality.

These differences in AEZs overlap with differences in cropping patterns and with thebroad regions of the slave trade. In Table 3, we show that regions that cultivated cerealgrains experienced the largest declines of slave exports in response to a temperatureshock, followed by those that cultivated roots and tubers. The effects of temperature areinsignificant for areas where agriculture was unimportant.10 These results have a similarinterpretation to the heterogeneous effects by AEZ. Cereal grains are more vulnerable toclimate change than roots and tubers (Lobell et al., 2008). Tree crops’ longer roots makethem better at nutrient uptake, and less responsive to a single year of weather variability(Nguyen et al., 2012). Furthermore, tree crops such as oil palm and coconut are veryresilient to heat stress, particularly short-lived heat stress, whereas popular tuber cropssuch as cassava, yam and sweet potato are only moderately heat-tolerant (Hartley, 1967;Kuo et al., 1993; Onwueme and Charles, 1994; Yamada et al., 1996). There is no evidencehere that African societies chose more durable crop types in drier AEZs. Cereal grains,for example, are dominant in both dry and moist savannah, while roots and tubers aremost important in both sub-humid zones and humid forest.

9Humidity data are from http://en.openei.org/datasets/node/61610We use the Murdock (1967) Ethnographic Atlas to identify the prevalent crop for each port. Each soci-ety’s dominant crop type is given by variable V 29. We take the modal crop for all societies within 500kmof each port. If there are no societies within 500km, we use the nearest society in the Atlas.


We also use Table 3 to show that broad differences exist across the major regions of theslave trade. The negative effects of temperature that we find are confined to Senegam-bia, the Bight of Benin, West-Central Africa, and Southeastern Africa. Some of the effectsize for West-Central and Southeastern Africa can be accounted for by their overwhelm-ing preponderance in the slave trade. Nunn (2008) estimates that Angola alone sentmore than three and a half million slaves across the Atlantic between 1400 and 1900.Consistent with our results by AEZ, all four of these regions include substantial portionsoutside Africa’s humid forest zone. Senegambia, West-Central and Southeastern Africaall contain substantial portions of savanna and grassland, while in the “Benin Gap,” thetropical forest opens and the savanna reaches the coast.

Together, these results suggest that the effects of temperature shocks on the slavetrade operated directly and indirectly through agricultural productivity, and were mostdeeply felt in the parts of Africa with the least resilient ecosystems.

3.2.2. Climate. In Table 4, we show that both the thirty-year moving average of temper-ature and fluctuations around it can explain slave exports. Both coefficients have nega-tive signs. Warmer trends and unusually warm years reduce slave exports. A one degreeanomaly over the 30-year climate mean has an average impact of nearly 1,300 fewerslave exports per port per year, similar to our main temperature measure, whereas aone degree increase in the 30-year climate mean has an average impact of nearly 18,000fewer slave exports per port per year. The impact of a warm trend is much larger thanan unusually warm single year. A one standard deviation change in within-port climatecauses about 1,800 fewer slaves to be exported per port per year on average.

Part of this difference may be purely mechanical. The within-port variance of thetemperature anomalies is greater than that of the climate anomalies, and the trendfor climate will smooth out year-to-year measurement error in temperature. However,the greater impact of a warming trend is also consistent with the mechanisms throughwhich we argue that environmental factors affected the slave trade. The cumulative im-pact of a warming trend on agricultural productivity and mortality are greater than for asingle warm year. Over time, these will lead to depopulation and out-migration, makingslave exports increasingly unviable. Though societies may adapt to sustained climatechange, a prolonged period of worsening climate can lead to social collapse (DeMeno-cal et al., 2001; Haug et al., 2003).

As an alternative to using the 30-year mean as a measure of “climate,” we also use aBaxter and King (1999) bandpass filter to decompose temperature into trends and cy-cles. Following their recommendations, we set the minimum period of oscillation to 2,the maximum period of oscillation to 8, and the lead-lag length of the filter to 3. Wereport results in Table 4. Using these measures, trends have the power to predict slaveexports, but shocks around them do not. This difference is not surprising, since thetrend computed using a bandpass filter is more sensitive to annual fluctuations in tem-perature.


3.2.3. Other possible mechanisms. Higher temperatures directly reduce agricultural pro-ductivity in Africa. In addition, they predict lower rainfall, which we are unable to ob-serve during the time period covered by our data. Our result, then, mixes the direct im-pact of temperature with indirect effects that operate through rainfall. To establish thesize of the correlation between temperature and rainfall, we use data on temperatureand precipitation from the University of Delaware.11 These report annual temperatureand precipitation figures for points spaced every 0.5◦ by 0.5◦ from 1900 to the present.We confine our analysis to points in Africa during the years 1900-2000. We regress thelog of annual rainfall on the log of annual temperature, point fixed effects and year fixedeffects. We find that a one percent temperature increase is associated with lower rainfallof 1.26 percent, with a standard error of 0.028. Though this is a large elasticity, temper-ature shocks explain less than 1% of the variance in rainfall fluctuations.12 While ourmain result captures the combination of higher temperatures and lower rainfall on thesupply of slaves, this suggests that the direct effect of temperature on agriculture andmortality is what drives our results.13

An alternative reading of our results would infer that higher temperatures were associ-ated with greater natural hazards for transatlantic shippers, and that our results do notreflect “supply side” shocks within Africa. As evidence against this interpretation, wemake use of additional data from the Trans-Atlantic Slave Trade Database. For 18,942voyages that have a known year of travel and a known region or port of slave purchase,the data also record whether the journey was completed successfully, failed due to a hu-man hazard, or failed due to a natural hazard. In this sample, we regress the occurrenceof a natural hazard on temperature, port fixed effects, and year fixed effects. To com-pute a temperature for ships without known ports, we assign ships to the modal port

11These are available at http://climate.geog.udel.edu/~climate/.12To show this, we begin by running the regression:

ln(Rainfallit) = δi + ηt + εRitHere, Rainfallit is the level of rainfall at point i in year t. δi is a point fixed effect. ηt is a year fixed effect.

We save the residuals from this regression. Call these εRit. We treat these residuals as “rainfall shocks”. Wethen run the regression:

ln(Temperatureit) = δi + ηt + εTitHere, Temperatureit is the temperature at point i in year t. Fixed effects are as defined above. We save the

residuals from this regression. Call these εTit. We treat these residuals as “temperature shocks.” We thenrun the regression:

εTit = β0 + β1εRit + εit

The R-squared of this regression is less than 0.01, suggesting that rainfall shocks only explain a smallfraction of the variance of temperature shocks. These shocks are correlated, but they do not move inlock-step. There is no justification for treating temperature as a proxy for drought alone.13We have also performed this same regression using levels, rather than logs, and using binary indicatorsfor whether rainfall or temperature are above their historical means. Both of these give results consistentwith the log specification.


in the region of slave purchase. We find that a 1◦C temperature increase reduces theprobability of a natural hazard by 10.4 percentage points, with a standard error of 3.5percentage points. Warmer years were associated with fewer natural hazards for thosewho shipped slaves across the Atlantic. Our main result works in the opposite direction,and overcomes this effect.

A third alternative explanation for our results is that wind speeds were higher in colderyears, which enabled ships to make a greater number of voyages than in warmer years.There are several reasons why this is not a main driver of our results. First, as discussedin section 3.2.1, the impacts of temperature are heterogenous by agro-ecological zone,which would not be the case if the results were driven by lower temperatures enablingthe ships to complete more voyages due to increased ship speeds.

Second, we use modern data on temperature and wind speed to show that highertemperatures only lead to small declines in wind speeds in the present. We use modernday (1950-2000) temperature and wind speed data from the Laboratoire de MeteorologieDynamique.14 We regress annual wind speed on annual temperature, controlling foryear and point fixed effects. A one degree Celsius increase in temperature leads to a 0.01meters/second (m/s) increase in wind speed globally, and a -0.02 m/s decrease in windspeed in the geographic region in Africa. These effects are quite small relative to themean wind speed, which is 3.2 m/s at the global level and 2.99 m/s around the regionnear Africa. Even though there is a negative association between temperature and windspeed in and around Africa, the magnitude is only about 1% of the mean, and it explainsvery little of the variation in wind speeds.15

It is also unlikely that voyage lengths are driving our result. Shippers had limitedscope to lengthen their buying periods in response to diminished African supply. Be-cause labor and borrowing costs increased with the length of a voyage, European traderswere keen to minimize their time on the African coast (Miller, 1996, p. 327). Miller (1981,p. 414) estimates that slaves in eighteenth-century Angola typically waited one monthin barracoons at the coast before being loaded onto a slave ship. Searing (1993, p. 80)similarly notes that shippers attempted to avoid risk and economize on feeding costs byminimizing the time between purchase and shipment. Harms (2008) describes the 1731voyage of The Diligent, a French slaving vessel. On several occasions, the ship left a WestAfrican port without purchasing slaves because the asking price was too high or becauseslaves would only be available after a delay.16 Of the voyages for which the time betweendeparture from home port and departure from Africa are known, fewer than 10% spentlonger than one year in Africa. A kernel density of this distribution is reported in onlineappendix F.

14A detailed explanation of the data and the analysis is available in online appendix B.15That is, regressing the partial residuals from a regression of wind speed on the point and year fixedeffects on the partial residuals from a regression of temperature on these same fixed effects gives an R-squared of 0.003.16See Harms (2008), pages 143, 148, 151, 152, and 212.


Another alternate interpretation of our findings would link higher temperatures withgreater productivity in cattle-keeping. In humid forest regions, higher temperatures in-crease the prevalence of tsetse flies, which increases morbidity and mortality of bothmen and cattle, due to the spread of sleeping sicknesses. In drier zones, however, highertemperatures kill the tsetse, benefitting cattle production (Pollock, 1982). We use threetests to show that this mechanism does not explain our results. First, we use the Mur-dock (1967) Ethnographic Atlas to identify the percentage of societies within 500km ofeach port who possess bovine animals.17 Including the interaction between tempera-ture and average bovine presence does not diminish the main effect (see Table A1, inthe online appendix). The interaction effect is positive, suggesting that the effect oftemperature is in fact weaker in areas that keep cattle.

Similarly, we use the Ethnographic Atlas to calculate the average dependence on an-imal husbandry for the societies within 500km of each port.18 Including the interactionbetween temperature and dependence on husbandry again does not diminish the maineffect (Table A1). The interaction is positive, but not significant. Third, we include theinteraction between temperature and the suitability of the area within 500km of eachport for tsetse.19 Yet again, this does not diminish the main effect (Table A1). The inter-action is positive, but not significant.

3.3. Robustness. We have tested the robustness of our main result to multiple checksfor unobserved heterogeneity, measurement of slave exports and temperature shocks,the unit of observation, outliers, the estimation method, and the inclusion of lag slaveexports as a control. The results of these tests are presented in the online appendix. Insome specifications, we were unable to compute clustered standard errors using tem-peratures, and so anomalies (with nearly identical point estimates) were used in theirplace. These are indicated in the tables.

3.3.1. Heterogeneity. To account for port-specific heterogeneity, we have allowed forport-specific linear trends and region-specific quadratic trends.20 These results are re-ported in Table A1. The addition of port and region-specific trends allow the right handvariables to evolve flexibly over time within a port or region.21 We also estimate (1) onthe sub-samples before and after the British abolition of the slave trade in 1807. This

17We use the latitude-longitude coordinates provided in the Atlas to identify the locations of these ethnicgroups. The presence of bovine animals is an indicator equal to 1 if variable V 40 is equal to 7, if V 40 isnon-missing. If there are no societies within 500km, we use the nearest society in the Atlas.18Dependence on husbandry is variable V 4. If there are no societies within 500km, we use the nearestsociety in the Atlas.19Tsetse suitability is raster data downloaded from http://ergodd.zoo.ox.ac.uk/paatdown/index.htm.This is only available for mainland Africa, and so these regressions exclude Madagascar and ports morethan 500km from the mainland.20Convergence could not be achieved with port-specific quadratic trends using the tobit estimator. Ifthese are included in an OLS estimation, the impact of temperature on slave exports remains negativeand significant.21In particular, they remove the need to include the interaction of the right-hand side variables by year.


shows both that a major break in the demand structure of the slave trade does not affectthe supply-side link between temperature and slave exports, and that the results sur-vive despite the relatively poor data available for individual ships after 1807.22 Similarly,discarding the years of the US Civil War does not meaningfully change the results.

Estimating the results separately for every 25-year interval in the data, we find a neg-ative coefficient in more than 90% of intervals. It is significant at the 5% level duringthe intervals centered from 1752-57, 1781-90, and 1835-1853. We find no evidence thatthe effect of temperature differed during years with El Nino events.23 We find no evi-dence that the effect of temperature varies according to whether shocks are positive ornegative relative to the port-specific mean over the 1730-1866 period (Table A1). Thisis consistent with present-day studies of African agriculture, which find that yields aredeclining in temperature, rather than being adversely affected by both warm and coldshocks (Exenberger and Pondorfer, 2011; Lobell et al., 2011).

We cannot estimate the effect of demand shifts in the slave trade as a whole, sincethese are collinear with the year fixed effects used in our principal specification. Wecan, however, account for port-specific changes in demand by destination region by in-cluding the temperature shock experienced at the nearest new world slave port. Theseports are, as in Nunn (2008), Virginia, Havana, Haiti, Kingston, Dominica, Martinique,Guyana, Salvador, and Rio. Similarly, we show that the results are robust to includingslave prices, both in the embarkation region and in the nearest new world port.24 Alter-natively, we use the disembarkation ports listed in the Trans-Atlantic Slave Trade Data-base to create a modal destination for each African port. Controlling for the anomaly atthese modal destinations also does not change the result. The correlation coefficientsof own and New World temperature anomalies, net of year and port fixed effects, are0.0905 for the nearest New World port and 0.0544 for the modal destination. Both aresignificant at the 1% level.

Controlling for the 30-year climate trend at the modal destination causes the coeffi-cient on temperature to fall by roughly 15%, though it remains significant. The coeffi-cient remains similar if we include temperature shocks experienced by the major slave-trading powers as controls. We compute these shocks by assigning each African port tothe country whose merchants shipped the greatest number of slaves from that port. We

22We discuss missing data in greater detail in online appendix A.23We identify El Nino events using the list provided by https://sites.google.com/site/

medievalwarmperiod/Home/historic-el-nino-events. This list uses Couper-Johnston (2000) as itsprincipal source.24Prices in Africa and the new world are taken from Eltis and Richardson (2004) and cover the years 1671-1810. There are many gaps in these series, especially for the New World ports. These are interpolatedlinearly using the values of the non-missing prices. For example, gaps in the prices of Senegambian slavesare imputed from the prices in the other African regions. The prices in Eltis and Richardson (2004) arereported for five year intervals. We treat prices as constant within these intervals. The impact of pricesthemselves in the regression is not statistically significant. The interpolation of prices within years as wellas across regions implies that by construction, their ability to reflect the impact of a localized shock in aparticular year is limited.


then compute a temperature shock for the major port of that country – Copenhagen,Nantes, Bristol, Amsterdam, Lisbon, Seville, or Virginia. The effect becomes larger inmagnitude and remains significant if we also control for the temperature shock at eachport’s nearest neighbor. The neighbor’s shock enters positively, suggesting diversionacross ports.25

We have also tested for several heterogeneous effects that we do not report. Inter-acting temperature shocks with mean slave exports gives suggestive evidence that theeffect is larger for more important ports, but this interaction term is marginally insignif-icant. We find no evidence in the cross-section of ports that a greater overall varianceof temperature shocks predicts greater average slave exports. We interact temperaturewith quintiles of terrain ruggedness. The effect is negative and significant in all inter-actions, and largest in the first and fifth quintiles. We find no heterogeneous effect bymalaria suitability. Finally, we do not find heterogeneous effects of temperature thatvary by the mean level of state centralization of the societies within 500km of each portrecorded in the Ethnographic Atlas.26

3.3.2. Measurement. We show that the method used to assign slaves to ports is not driv-ing the results. We use only the slaves from known ports to calculate port-by-year ex-ports, and achieve similar results to our baseline approach. The effect is smaller, but inproportion to the smaller standard deviation of the dependent variable. The results alsosurvive when using slaves from known ports or regions only. Results are similar if weuse slaves disembarked in the new world, rather than slaves embarked from Africa. Re-sults remain negative and significant if slave exports are normalized by the populationdensity of the area within 500 km of each port in 1700.27

Similarly, we show that our results are not an artefact of the bilinear interpolationused to construct port-specific temperatures. We can use the temperature calculatedfrom the closest point in the temperature data and achieve similar results to our base-line. We receive very similar results if we discard temperature points located over theocean when joining ports to their nearest temperature point. We use the natural log oftemperature as an alternative measure of weather shocks, in order to account for pos-sibly multiplicative measurement error. The result is still negative and significant. It isalso negative and significant if the log of (one plus) slave exports is used as the depen-dent variable.

25Although ports are typically close to their nearest neighbors (mean = 75.5km) some are more distant(s.d. = 203km, max = 1, 643km).26This is variable V 33, if V 33 is non-missing. If there are no societies within 500km, we use the nearestsociety in the Atlas.27Historical population density is taken from the History Database of the Global Environment (HYDE)version 3.1. This raster data on historical population can be downloaded from ftp://ftp.mnp.nl/hyde/

hyde31_final. Documentation of the data is provided elsewhere (Bouwman et al., 2006; Klein Goldewijk,2005; Klein Goldewijk et al., 2010).


Because we do not know the slave catchment areas for each port, we measure tem-perature shocks at ports rather than in the interior. As an alternative, we compute tem-perature shocks experienced by the ethnic groups surrounding each port. For each port,we identify the ethnic groups mapped by Murdock (1959) that have centroids within 500km of of the port. For each of these groups, we use the temperature point closest to thegroup’s centroid to compute annual temperatures. For these same groups, Nunn andWantchekon (2011) report the number of slaves exported across the Atlantic over thecourse of the entire slave trade. We use these sums to weight the temperature shocks forthe ethnic groups surrounding each port, thus constructing an “interior ethnic groups”shock for each port. As reported in the online appendix, these interior shocks have aneffect with a magnitude close to our baseline. Results are similar if cutoffs of 250km or1000km are used for assigning ethnic groups to ports (not reported).

We also validate the use of temperatures at coastal ports as a proxy for conditions inthe interior by showing that temperature shocks in modern data are strongly correlatedover space. We collect data on annual African temperatures from 1980-2000, reportedon a 0.5◦ by 0.5◦ grid by the University of Delaware.28 To make the estimation computa-tionally feasible, we reduce the resolution of this data to a 3◦ by 3◦ grid. Creating everypairwise merge between ports in the data, we test whether temperatures at point j affecttemperatures at point i by estimating:

(2) temperatureit =K∑k=1

βkDij,k × temperaturejt + δij + ηt + εijt

Here Dij,k is a dummy variable for whether point i and j are within distance bandk. We use 100 kilometer distance bands (200-300 km, 300-400 km, and so on). δij isa fixed effect for each pair i, j. ηt is a year fixed effect, and εijt is error. We show inthe online appendix (Table A4) that shocks are remarkably persistent across space. Forexample, the βk corresponding to a distance band of 500 km to 600 km suggests thata 1◦C shock between 500 and 600 km away raises local temperature by slightly morethan 0.5◦C. Temperatures measured at ports, then, are valid proxies for conditions inthe interior.

3.3.3. Level of observation. Our results are not sensitive to the use of ports as the unitof observation. We collapse the African coastline into grid squares one degree in longi-tude by one degree in latitude. We take the sum of all slaves exported from within thatgrid square as slave exports, and the average temperature for ports within that squareas the temperature for that square. The results are very similar to our baseline specifi-cation. Results are similar if they are collapsed into squares five degrees by five degrees.This is equivalent to collapsing to the nearest point in the climate data. Similarly, ifwe collapse slave exports into the major regions of the slave trade (Senegambia, Sierra

28These are available at http://climate.geog.udel.edu/~climate/.


Leone, the Windward Coast, the Gold Coast, the Bight of Benin, the Bight of Biafra, West-Central Africa, and Southeastern Africa), again using the average temperature acrossports within a region to measure the aggregated temperature, we find a large negativeimpact of temperature on slave exports. Our main result holds if ports are collapsed bythe ethnic groups into which they fall, as mapped by Murdock (1959). If we collapse thedata into five-year averages, the results are again similar to the baseline.

3.3.4. Outliers. We discard statistical outliers, re-estimating the results using ordinaryleast squares (OLS), calculating dfbeta statistics, and then re-estimating the main tobitspecification without observations whose absolute dfbeta is greater than 2/

√N .29 Simi-

larly, we show that we can achieve our main results without relying on certain subsets ofthe data. We eliminate the smaller ports in the sample by removing the bottom 50% ofports by total number of slaves exported. We also show that the results are not driven byinactive ports by excluding all observations from the data where a port has either ceasedto export slaves, or has not yet begun its participation in the trade.

3.3.5. Estimator. We employ several alternative estimation strategies. We begin by re-estimating the main equation using OLS. The effect of a temperature shock remainsnegative and significant. Unsurprisingly, the estimated effect is smaller if we do notaccount for censoring. Using Conley (1999) standard errors and allowing spatial depen-dence over distances of up to 10 decimal degrees, the estimated standard error rises,but the result is still significant at the 5% level. We also find a significant and negativeeffect of temperature when discarding observations with no slave exports or includinglagged temperature as a control.30 Using a binary indicator for nonzero slave exports asthe dependent variable, we again find a negative effect of temperature. Dividing this byquintiles of mean exports, we find a negative and significant response to temperaturealong the extensive margin for the lowest three quintiles, and a negative insignificantresponse at the top two (not reported). The coefficient estimate remains large and neg-ative if the running maximum of slave exports added to the baseline as an additionalcontrol; the same is true when including ten-year running means of temperature or itsvariance (not reported).

The number of observations is large relative to the number of fixed effects, and so theincidental parameters problem should only be a minor concern. However, because (1)is non-linear, Wooldridge (2002, p. 542) suggests including port-specific mean temper-atures temperaturei rather than port fixed effects. Under the assumption that the portfixed effects δi are linearly related to the port-specific means (δi = ψ+ai+λtemperaturei),

29The standard test of discarding high-leverage observations is not reported. Since no observations haveleverage greater than 2(df + 1)/N , these results are identical to the main specification.30Including lagged temperature does not change the coefficient on the contemporaneous year’s temper-ature. The impact of lagged temperature are smaller than the impact of the contemporaneous year’stemperature, and are not statistically significant after two lags. Both lags are also negative, so we find noevidence that societies compensate for a low-export year by exporting more slaves the following year.


this will give consistent estimates of β. The results are congruent with our baseline spec-ification. We find a negative but marginally insignificant (p = 0.13) effect of temperatureif we replace the year fixed effects with a quadratic time trend in our baseline specifica-tion. We also find a negative and significant effect of temperature using a Poisson model(β = −1.096, s.e. = 0.178).31

3.3.6. Inclusion of lag slave exports. We include lagged slave exports as a control. Sinceslave exports in the previous year are correlated with the error term, we use the differ-ence between slaves exported two years ago and slaves exported three years ago as in-struments for lagged slave exports. Although the coefficient estimate is smaller than inthe baseline, the results again suggest a sizable reduction in slave exports during warmeryears. Roughly 1,900 fewer slaves are exported per port in a year with a 1◦C rise in tem-perature.

Wooldridge (2005) suggests that censored models with a lagged dependent variablesuch as ours can be estimated by including lagged slave exports, mean temperature, andinitial slave exports in the estimation. This is consistent under the assumption that theport-level fixed effects δi can be decomposed into δi = ψ+ai+λ1slavesi0+γtemperaturei.This decomposition assumes a relationship between the initial number of slaves fromwhen the trade first started and the port-fixed characteristics and reduces it to a regulartobit estimation. Here too, warmer temperatures predict a sizeable reduction in slaveexports, about 1,300 slaves per port in a year with a 1◦C temperature shock.

Re-estimating the same specification using the Arellano-Bond estimator (using twolags as an instrument), we find that the estimated coefficient on temperature is verysimilar to the estimate obtained using OLS. This is larger than the coefficient obtainedby including the lagged dependent variable and estimating the effect using OLS. Thissuggests that, if there is any bias on the estimated coefficient on temperature when in-cluding the un-instrumented lag, it is towards zero, understating the effect of tempera-ture on slave supply.

4. PERSISTENCE

While colder years improved agricultural productivity, they also increased slave ex-ports. The density of modern night-time lights – a proxy for economic activity – can beused to identify which effect dominated over the long run.32

Following Michalopoulos and Papaioannou (2013) and Henderson et al. (2012), weuse night-time lights as a proxy for modern development. These have the advantage ofovercoming the lack of reliable sub-national data on economic activity in sub-SaharanAfrica (Jerven, 2013). These data are taken from the Defense Meteorological SatelliteProgram’s Operational Linescan System. Henderson et al. (2012) provide a particularly

31Convergence could not be achieved using a negative binomial model.32These were originally downloaded from http://www.ngdc.noaa.gov/dmsp/global_composites_v2.

html, and have since been moved to http://www.ngdc.noaa.gov/dmsp/downloadV4composites.html.


detailed description of the data. These data are collected by capturing satellite imagesof the earth between 20:30 and 22:00 local time and averaging them over the course ofthe year. The raw data are at a 30 second resolution, so that each pixel is roughly onesquare kilometer. Luminosity for each pixel is reported as a six-bit integer ranging from0 to 63. For each of our 134 ports, we calculate the average light density in 2009 for pixelswithin 500 km.

We then use OLS to estimate:

(3) ln(lightdensityi) = βweightedanomalyi + x′iγ + εi.

Here, weightedanomalyi is the weighted sum of the temperature anomalies over theslave trade as a whole, weighted by the number of slaves exported from all ports in aparticular year. That is:

(4) weightedanomalyi =∑t

slavest × anomalyit∑t slavest

Alternatively, we report specifications that average the anomalies over selected pe-riods. As in our main analysis, the anomaly is signed; positive values indicate yearsthat are warmer than the 1902-1980 mean, while negative values indicate years that arecolder than the 1902-1980 mean. If β > 0, it would indicate that the net effect of un-usually warm weather experienced during the slave trade was beneficial for moderndevelopment. That is, over the long run, the beneficial effects of limiting slave exportsoutweighed the adverse effects of temporarily reduced agricultural output.xi is a vector of controls that includes a constant, absolute latitude, longitude, the

number of raster light density points within 500km of the port, dummies for AEZs, dis-tance from the nearest Atlantic or Indian Ocean port of slave demand, and average tem-perature over the period 1902-1980. Standard errors are clustered by the nearest climatepoint. We report our results in Table 5. Past temperature shocks predict higher incomesin the present, suggesting that, over the long-run, the effects of the reduction in slave ex-ports out-weigh those of the losses to agriculture. In particular, it is temperature shocksduring the late eighteenth century peak of the slave trade that best predict luminosityin the present.

These results echo those of Nunn (2008) at a local level; the slave trade hinderedAfrican development over the long run. Temperature is one of the many variables thataffected participation in the slave trade. The literature has proposed multiple mecha-nisms for this, and we test several possibilities in Table 5. First, Fenske and Kala (2014)have shown a long-run effect of nineteenth-century slave exports on conflict in thepresent day. Drawing 500km circles around each of the ports in our data, we show thatareas that experienced greater temperatures at the peak of the slave trade experienceless violence in the present. We use two separate measures of violence: battles recorded


for the period 1997 to 2013 in the Armed Conflict Location and Event data project,33 andbattle deaths for the period 1989 to 2010 recorded in the UCDP Georeferenced EventDataset, version 1.5-2011.34 Both measures of conflict are negatively correlated withweightedanomalyi.

Nunn and Wantchekon (2011) suggest, alternatively, that African ethnic groups thatexported more slaves are less trusting in the present. We use the same Afrobarometerdata that they use to show that areas that experienced higher temperatures during theslave trade are more trusting today.35 We average the answers of respondents within500km of each port to question 83, “Generally speaking, would you say that most peoplecan be trusted or that you must be very careful in dealing with people?” A value of 0corresponds to the answer “you must be very careful,” while a value of 1 correspondsto the response that “most people can be trusted.” This measure of trust is positivelycorrelated with weightedanomalyi.

Obikili (2013b) and Whatley (2012) have argued that the slave trade increased the frag-mentation and absolutism of traditional political authorities. We use the fourth roundof the Afrobarometer to show that higher temperatures during the slave trade predictbetter traditional and local governments today.36 We average the answers of respon-dents within 500km of each port to two questions. The first, 54c, asks respondents “Howmuch of the time do you think the following try their best to listen to what people likeyou have to say: Traditional leaders.” Answers range from 0 (never) to 3 (always). Thesecond, 70c, asks respondents to rate the performance of their local government coun-cilor on a scale from 1 (strongly disapprove) to 4 (strongly approve). Both measures oflocal government quality are positively correlated with weightedanomalyi.

Finally, Dalton and Leung (2013) and Edlund and Ku (2014) have suggested that theslave trade changed the role of women in African society. We use the Demographic andHealth Surveys to show that higher temperatures during the slave trade predict betteroutcomes for women today.37 We average the answers of respondents within 500km ofeach port to two questions. The first, 511, asks respondents their age at first marriage.

33http://www.acleddata.com/data/34http://www.pcr.uu.se/research/ucdp/datasets/ucdp_ged/35Survey data are taken from http://www.afrobarometer.org/. Co-ordinates are available from http:

//scholar.harvard.edu/files/nunn/files/afrobarometer_r3_location_data.zip.36Survey data are taken from http://www.afrobarometer.org/ To our knowledge, no co-ordinates existfor respondents. Rather, we use the ethnicity centroids reported by Deconinck and Verpoorten (2013) athttp://qed.econ.queensu.ca/jae/2013-v28.1/deconinck-verpoorten/37Data are available from http://dhsprogram.com/Data/. Our sample consists of the most recent Indi-vidual Recode standard DHS data for each country that have geographic coordinates. If no standard DHSdata are available, we use the most recent Malaria Information Survey that has geographic coordinates.In particular, we use Angola 2011, Benin 2011-12, Burkina Faso 2010, Cameroon 2011, Central AfricanRepublic 1994-95, Congo Democratic Republic 2007, Cote d’Ivoire 2011-12, Ethiopia 2011, Ghana 2008,Guinea 2012, Kenya 2008-09, Lesotho 2009, Liberia 2007, Madagascar 2008-09, Malawi 2010, Mali 2006,Mozambique 2011, Namibia 2006-07, Niger 1998, Nigeria 2013, Rwanda 2010, Senegal 2010-11, Tanzania2011-12, Togo 1998, Uganda 2011, Zambia 2007, and Zimbabwe 2010-11.


The second, v212, asks their age at first birth. Both measures of female empowermentare positively correlated with weightedanomalyi.

In Table A6 in the online appendix, we report robustness checks. Results are similarif ports that do not export slaves over our time period are discarded, and if a control isadded for whether the port is in the national capital. Our principal specification withcontrols remains significant at the 10% level when Conley (1999) standard errors areadjusted for spatial dependence over distances of ten decimal degrees. In Table A7, wereport additional checks. First, we show that greater anomalies do not predict greaterluminosity at points along the sub-Saharan coast that are not near slave ports.38 Theunconditional correlation is much smaller, and turns negative once additional controlsare added. Second, we show that the result survives additional controls – distance fromthe national capital, distance from the nearest foreign border, petroleum, and malariasuitability.39

5. INTERPRETATION

5.1. Argument. We argue that higher temperatures raised the cost of slave capture andexport. Consider a coastal African ruler who maximizes profits from selling slaves, asin Fenoaltea (1999). The ruler “produces” a quantity S of slaves using an army that hecontrols. He may or may not be a price taker, and traders at the coast will pay p(S) perslave. We assume the inverse demand function is downward-sloping: ps ≤ 0. The costof raiding for S slaves is C(S, T ), where T is temperature. Costs are convex in both thequantity of slaves exported and in temperature. That is, CS > 0, CSS > 0, CT > 0, andCST > 0. The ruler, then, will choose S to maximize p(S)S −C(S, T ). So long as demandis not “too convex,”40 temperature reduces exports:

dS

dT=

CST

pssS + 2ps − CSS

< 0.

The critical assumption is that CST > 0. We believe this for four reasons. First, theruler’s costs of extracting tribute in order to feed a slave-harvesting army rise during badharvests. This can be due to greater peasant resistance, or to greater prices of food in theinterior. At Luanda, for example, prices of provisions were responsive to weather shocks(Miller, 1996, p. 397). Second, the mortality of slaves, soldiers and porters will rise inwarmer years. In addition, with greater morbidity, the ruler’s cost of providing slaves of

38These are taken from a set of 500 station points at equal intervals on the African coastline. This numbergives them a spacing roughly equal to that of the slave ports.39Distance from the national capital is computed using the sphdist function in Stata. Distance fromthe nearest foreign border is computed using ArcMap. Petroleum is an indicator for whether the portoverlaps with an oilfield mapped in http://www.prio.no/CSCW/Datasets/Geographical-and-Resource/

Petroleum-Dataset/Petroleum-Dataset-v11/. Malaria suitability is the average within 500km, asmapped by www.map.ox.ac.uk.40That is, pssS + 2ps − CSS < 0.


any given quality will rise. Third, higher temperatures lead to greater evapotranspira-tion, increasing the probability that drought will set in. Areas of slave supply becomemore disordered, raising the costs of raiding directly. Finally, the slave trade dependedon complementary economic activities that provisioned ships, fed the populations ofthe ports, and supplemented the incomes of slave traders.

There are a priori mechanisms that predict a higher slave supply in worse agricul-tural years, such as people selling themselves or family members into slavery to avoidstarvation. However, our findings strongly suggest that the net impact of slave exportsin years of higher temperature was negative. While we discuss these opposing mecha-nisms briefly, we focus on the mechanisms that contribute to the negative impacts onslave exports that we find dominate in the results.

5.2. Temperature, agriculture, and mortality. There is substantial evidence that tem-perature shocks affect agriculture and mortality in the present. Studies of the impactof climate on modern agricultural productivity in Africa (Kala et al., 2012; Kurukula-suriya and Mendelsohn, 2008) indicate that higher temperatures relative to the base-line climate have a negative impact on productivity, particularly for non-irrigated agri-culture. In addition, higher temperatures increase evapotranspiration (Brinkman andSombroek, 1996). This indicates that colder years lead to a relatively higher level of wa-ter availability for plants, which is crucial in certain stages of plant growth. Similarly,organic matter in the soil decomposes faster in higher temperatures (Bot and Benites,2005). Other studies of temperature impacts on the productivity of tropical agriculturefind similar results (Guiteras, 2009; Sanghi and Mendelsohn, 2008) Thus, the link be-tween colder years and higher agricultural productivity in the tropics is well established.

There is also evidence that higher temperatures increase disease burdens that raisemortality (Burgess et al., 2011). Studies of the relationship between disease and temper-ature find that higher temperatures are more conducive to the spread and transmissionof diseases such as malaria and yellow fever (Alsop, 2007). Malaria and yellow fever haveplaced a particularly heavy mortality burden on Africa throughout the continent’s his-tory (Gallup and Sachs, 2001; Ngalamulume, 2004). Further, arid AEZs and modern-daychild malnutrition are positively correlated (Sharma et al., 1996).

5.3. Case studies. The histories of Benguela, Whydah, and Mozambique are consistentwith our interpretation of our empirical findings. These three cases are statistically in-fluential, well documented, and come from three separate regions. For each, we de-scribe the effects of adverse weather and outline the interdependence between the slavetrade and the broader economy.

5.3.1. Benguela. Between 1695 and 1850, Benguela sent nearly half a million slaves tothe new world (Candido, 2006, p. 18). In West-Central Africa, adverse climate eventsreduced slave exports through three main channels. First, the resources available forslave capture were greater in good years. Military forces timed their expeditions to take


advantage of the seasonal availability of ripening fields and full granaries for plunder(Miller, 1996, p. 48, 147). Portuguese soldiers in the interior were often without a regularsalary, and so exchanged gunpowder inland for chickens and other agricultural prod-ucts (Candido, 2006, p. 38). Military officials bought food and other commodities usingtrade goods such as beads and textiles (Candido, 2006, p. 112). Slaves were marched tothe coast by caravan, and caravan porters used these as opportunities to trade on theirown accounts (Candido, 2006, p. 124).

Second, periods of higher temperatures, in addition to providing fewer trade opportu-nities, would have been times of greater mortality for both slaves and porters. The mor-tality of slaves between capture and the coast may have been over 50% in eighteenth-century Angola (Miller, 1996, p. 120). After one long drought period, many slaves inLuanda were sick and dying (Miller, 1996, p. 178).

Third, droughts produced “violence, demographic dispersal, and emigration” (Miller,1982, p. 32). Confrontation between Portuguese forces and African states occurred with“suspicious regularity at the end of periods of significantly reduced precipitation” (Miller,1982, p. 24). Tribute from local Sobas was often rendered in the form of slaves (Can-dido, 2006, p. 24), and so disruption to the political order constricted the flow of slaves.Famines pushed Africans to resettle in more distant regions (Candido, 2006, p. 48), rais-ing the costs of capture. The movement of villages in response to drought was so fre-quent in South-Central Africa that permanent dwellings were rarely built (Miller, 1996,p. 157).

5.3.2. Whydah. Whydah was Dahomey’s principal port. Congruent with our model, theprincipal sources of slaves after 1730 were capture by the Dahomean army and purchasefrom the interior (Law, 2004, p. 138). The success of the slave trade, then, responded tothe resources available to the state. Dahomey competed with other states of the “SlaveCoast” to supply slaves for the Atlantic trade (Law, 2004, p. 126). Conflict was seasonal,as wetter periods increased the threat that tsetse flies posed to Oyo’s cavalry (Law, 1975).Middlemen supplemented the royal trade by purchasing slaves from neighboring areas(Law, 2004, p. 111). Their ability to acquire slaves was tied to conditions in regions ofslave supply; in the 1770s and 1780s, for example, disturbances on the coast made itdifficult to buy slaves in eastern markets (Ross, 1987, p. 370). Middleman trade alsodepended on the strength of the Dahomean army. It was the Dahomean conquest ofalternative ports such as Jaquin and Apa that drove trade towards Whydah (Ross, 1987,p. 361).

The slave trade was supported by local retail, agriculture, fishing and salt-making(Law, 2004, p. 77). The city depended on goods imported from the interior that wereconsumed locally, including kola from Asante and natron from Borno (Law, 2004, p. 83).The trade itself depended on the labor of local porters, water-rollers, laundry women,and other workers (Law, 2004, p. 147). Adverse shocks to these other sectors, includingweather shocks, raised the costs of provisioning the slave trade.


5.3.3. Mozambique. Slave exports from Mozambique Island accelerated from the 1770sand grew until the 1830s (Newitt, 1995, p. 245-6). Severe “mahlatule” droughts occurredfrom 1794 to 1802 and from 1823 to the late 1830s. Local people intensified activi-ties such as hunting, gold mining and trading. When these failed, they turned to out-migration, which led to instability, war, banditry and slaving (Newitt, 1995, p. 253). Thelong second drought upended peasant life, and much of the population starved, diedof smallpox or moved elsewhere (Newitt, 1995, p. 254). This made slave capture morecostly.

By disrupting settlement patterns, trading networks, and local states, droughts raisedthe costs of slaving. The Nguni states that were pushed north of the Zambezi by themahlatule were known for their fierceness and economic self-sufficiency, which iso-lated the region from trade (Newitt, 1995, p. 264). Droughts slowed Portuguese move-ment into the interior and made rivers transport difficult (Newitt, 1995, p. 255, 264, 284).

The island depended on food from the mainland (Newitt, 1995, p. 190). Slave shipswere similarly dependent on local food and supplies (Newitt, 1995, p. 249). These needswere keenly felt in periods of bad weather; the island was forced, for example, to importfood during the drought in 1831 (Alpers, 2001, p. 77).

6. CONCLUSION

We find that environmental shocks within Africa influenced the dynamics of the slavetrade. The effects we find are large. A temperature increase of one degree Celsius re-duced annual exports by roughly 3,000 slaves per port. We interpret these as shifts inthe cost of slave supply, operating through mortality and the productivity of comple-mentary sectors. The histories of Benguela, Whydah, and Mozambique support ourinterpretation. Past temperature shocks predict economic activity today.

We have advanced the existing understanding of Africa’s participation in the slavetrade by incorporating previously unutilized, time-varying measures of weather shocksspanning all sending regions. This exercise demonstrates the importance of supply-sidefactors in the dynamics of the transatlantic slave trade. This has also enabled us to pro-vide new evidence on the channels through which geography shapes economic devel-opment in a historical setting. We are able to examine the responsiveness of a differentform of conflict to economic shocks than is typically studied in the literature. Ratherthan being encouraged by economic distress, slave raiding was hindered by it.

There are, of course, limitations to our approach. Data availability prevent us fromlooking at the dynamics of the Indian Ocean, Red Sea, or internal African slave trades.Similarly, we are unable to examine the period before 1730, or environmental factorsother than temperature. Further, our results should not be over-extrapolated. Depend-ing on their resource endowments and institutions, societies may adapt to change, par-ticularly to slow-moving changes in climate. As climate scientists advance in their re-construction of the environmental past, we are hopeful that it will become possible to


examine these issues further and to better understand the long-run causes of develop-ment.

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(1) (2) (3) (4) (5)

Mean s.d. Min Max N

Slaves exported 444 1,813 0 34,927 18,358

Slaves (non-zero) 2,543 3,673 1.23 34,927 3,206

Temperature (interpolated) 25.2 2.33 13.3 27.5 18,358

Temperature (closest point) 25.2 2.34 13.3 27.4 18,358

Climate (30 year mean temperature) 25.2 2.32 13.4 27.3 18,224

Deviation from 30 year mean temperature -0.00043 0.13 -0.86 0.62 18,224

Year 1,798 39.5 1,730 1,866 18,358

AEZ: Desert 0.030 0.17 0 1 18,358

AEZ: Subhumid 0.28 0.45 0 1 18,358

AEZ: Forest 0.43 0.50 0 1 18,358

AEZ: Dry Savannah 0.15 0.36 0 1 18,358

AEZ: Moist Savannah 0.11 0.32 0 1 18,358

Table 1: Summary statistics

(1)

Temperature -3,052.058***

(1,114.903)

Year F.E. Y

Port F.E. Y

Observations 18,358

Clusters 28

Notes : ***Significant at 1%, **Significant at 5%, *Significant at 10%. Standard errors clustered by

closest climate point in parentheses. The dependent variable is slave exports. All regressions are tobit.

† Anomaly used in place of temperature.

Table 2: Main results

(1) (2) (3) (4) (5)

Temperature X Temperature (interpolated) -3,148.175***

Desert -3,862.564*** (980.134)

(1,287.854) Temperature X

Dry Savannah -3,924.755*** Humidity above median 3,233.835***

(937.347) (962.822)

Sub-Humid -2,643.042* Senegambia -1,475.200**

(1,367.530) (642.892)

Moist Savannah -1,570.850* Sierra Leone 389.839

(824.036) (996.694)

Humid Forest 239.182 Windward 2,322.019

(1,289.700) (1,633.087)

Cereals -3,472.447*** Gold Coast -982.343

(1,182.922) (1,228.014)

Roots -2,715.040 Benin -2,856.152*

(1,963.234) (1,692.232)

Trees 1,893.372* Biafra 379.252

(1,099.699) (1,404.252)

None 676.444 West-Central -3,792.429***

(1,373.256) (895.225)

Southeast -5,565.669**

(2,181.252)

Year F.E. Y Y Y Y

Port F.E. Y Y Y Y

Obs. 18,358 18,358 18,358 18,358

Clusters 28 28 28 28

Cereals None Roots and Tubers Tree Fruits

Desert 25.00 0.00 50.00 25.00

Dry Savannah 95.00 5.00 0.00 0.00

Sub-Humid 41.38 3.45 55.17 0.00

Moist Savannah 93.33 0.00 6.67 0.00

Humid Forest 37.84 0.00 48.65 13.51

Total 53.73 2.24 39.55 4.48

Table 3: Results by region

Notes: ***Significant at 1%, **Significant at 5%, *Significant at 10%. Standard errors clustered by closest climate point in

parentheses. The dependent variable is slave exports. All regressions are tobit. † Anomaly used in place of temperature.

Distribution of crop types by AEZ

(1) (2) (3)

Deviation from temperature normal -1,244.187** -2,640.058***

(529.643) (877.715)

Temperature normal -18,584.020*** -20,727.839***

(6,904.375) (7,297.426)

Obs. 18,224 18,224 18,224

Clusters 28 28 28

BK Filter Temperature Shock -854.416 11.203

(560.692) (497.584)

BK Filter Temperature Trend -7,222.670*** -7,224.724***

(2,279.920) (2,280.156)

Obs. 17,554 17,554 17,554

Clusters 28 28 28

Year F.E. Y Y Y

Port F.E. Y Y Y

Table 4: Climate

Notes: ***Significant at 1%, **Significant at 5%, *Significant at 10%. Standard errors clustered by

closest climate point in parentheses. The dependent variable is slave exports. All regressions are tobit.

† Anomaly used in place of temperature.

Climate computed as 30 year moving average

Climate computed using bandpass filter

(1) (2) (3) (4) (5) (6) (7) (8)

Anomaly 0.794* 0.721** 0.065* 0.050* 0.139* 0.059 0.166** 0.050*

(0.462) (0.337) (0.034) (0.029) (0.071) (0.040) (0.066) (0.026)

Controls N Y Y Y Y Y Y Y

Time Period Weighted Weighted 1730s 1740s 1750s 1760s 1770s 1780s

Obs. 120 120 134 134 134 134 134 134

Clusters 26 26 28 28 28 28 28 28

R2 0.137 0.495 0.482 0.478 0.484 0.464 0.500 0.480

Anomaly 0.037 0.050 0.019 0.036 0.044 0.031 0.035 0.047

(0.025) (0.033) (0.033) (0.042) (0.036) (0.026) (0.028) (0.039)


Time Period 1790s 1800s 1810s 1820s 1830s 1840s 1850s 1860s

Obs. 134 134 134 134 134 134 134 134

Clusters 28 28 28 28 28 28 28 28

R2 0.465 0.474 0.444 0.449 0.458 0.456 0.461 0.459

Battles Battle Deaths Trust

Traditional

Authorities

Listen

Performance

of Local

Council

Age at first

marriage

Age at first

birth

Anomaly -2,957.755*** -21.192** 0.183** 1.011*** 0.453** 1.868*** 4.471***

(484.963) (7.575) (0.080) (0.125) (0.175) (0.590) (0.580)

Controls Y Y Y Y Y Y Y

Time Period Weighted Weighted Weighted Weighted Weighted Weighted Weighted

Obs. 120 115 80 103 103 111 115

Clusters 26 23 21 21 21 22 23

R2 0.650 0.304 0.669 0.596 0.790 0.856 0.879

Notes: ***Significant at 1%, **Significant at 5%, *Significant at 10%. Standard errors clustered by closest climate point in parentheses. All

regressions are OLS. The dependent variable is log light density. All regressions include a constant. Controls are absolute latitude, longitude, the

number of raster light density points within 500km of the port, dummies for AEZs, distance from the nearest Atlantic or Indian Ocean port of slave

demand, and average temperature over the period 1902-1980.

Table 5. The modern impact of past temperature anomalies

Panel A. Dependent variable is log light density

Panel B. Other Modern Outcomes

Online appendix: Not for publication.


APPENDIX A. MISSING DATA

We use the ship-level data to describe the variables that predict data quality in TableA5. We use whether the principal port of slave purchase is missing as an indicator of dataquality. Without adding additional controls, it is clear that the data improve in qualityfrom 1500 to 1550, before declining steadily to 1750. Data begin to improve again after1750, only to become worse after the suppression of the slave trade in the early 1800s.However, these trends are confounded by the changing composition of the slave tradeover time, across national carriers, and regions of slave purchase. Relative to Britishships, French and Portuguese carriers are less likely to lack data on the port of principalslave purchase. Controlling for time, however, reveals the Portuguese data to be of alower quality. Relative to Southeast Africa, data from other regions, excepting the GoldCoast, tend to be of worse quality.

In addition, there are 20,143 voyages occurring after 1729 for which the major regionof slave purchase is known. We merge these to the annual temperatures of the regionsin our data, averaged over ports within each region. We regress whether the port ofslave purchase is missing on temperature, region fixed effects, and year fixed effects. Wefind that a one degree temperature increase predicts a 2.60 percentage point reductionin the probability that the port of slave purchase is missing. The heteroskedasticity-robust standard error of this estimate is 3.05 percentage points, making it insignificantat conventional levels.

APPENDIX B. IMPACTS OF TEMPERATURE ON WIND SPEED

We use data from on modern temperature and wind speed from the NCC (NCEP Cor-rected by CRU) model, housed at the Laboratoire de Meteorologie Dynamique (Ngo-Duc et al., 2005). This is a global model at the 1◦ by 1◦ level, with observations availableat 6-hourly intervals from 1948-2000 (We use the years 1950-2000). Ngo-Duc et al. pre-pare these data using satellite data as inputs into a global circulation model, correctingthem using station-level data from the Climate Research Unit at East Anglia. Our regres-sion specification is:

windit = α + βtemperatureit + pointi + yeart + εit,

where windit and temperatureit is the mean annual wind speed in m/s and mean an-nual temperature in degrees Celsius at point i in year t, respectively, pointi is the pointfixed effect, and yeart is the year fixed effect. We run this specification both at the globallevel, and for the region around Africa (which restricts latitude to between -50 and 50and longitude to between -40 and 60).

APPENDIX C. DETAILED DESCRIPTION OF THE TEMPERATURE DATA

We use the temperature data constructed by Mann et al. (1998a,b), a multi-proxy grid-ded series of annual temperature shocks (relative to 1902-1980) reconstructed from the


year 1400 onwards. The authors use previously available long instrumental records ofa variety of proxy indicators, such as dendroclimatic records, ice cores, and ice meltrecords, and combine them in a single multi-proxy series of temperature records. Thisconstruction of a single time-series for each 5◦ by 5◦ point takes into account the uniqueuncertainties and reconstruction issues for each proxy indicator, and the presence ofmultiple and independent sources of proxy data implies that their estimates are rel-atively robust to the limitations of using a single source of paleoclimatic data. Fur-thermore, they use available instrumental temperature records from the early twentiethcentury, 1902-1995 in particular, to calibrate the historical estimates of temperature.

The variability in the modern instrumental temperature measurements is decom-posed into eigenvectors, each of which has an associated empirical orthogonal func-tion (EOF) which describes its spatial variability as well its principal component (PC)that describes its temporal evolution. The first five of these eigenvectors explain 93%of the variation in global mean temperatures. Then, each of the historic proxy recordsare calibrated using these eigenvectors separately, and the reconstructed multi-proxytemperature series is obtained using optimization methods to determine the optimalcombination of eigenvectors represented by the multi-proxy data.

An advantage of using this approach is that known phenomena affecting long-rangepatterns of variability such as the El Nino/Southern Oscillation (ENSO) can be exploitedto reconstruct temperature in areas for which paleoclimatic records are not directlyavailable. This is done by using the known form of these teleconnections and the pres-ence of paleoclimatic records in locations that are linked through these patterns. Theresults obtained were verified using numerous robustness checks, including examiningregion-level data with the availability of very long instrumental records, as well as theability of the series to reproduce known historical events such as the 1791 strong El-Nino year and the 1815 Tambora volcano eruption that caused lower temperatures in1816.


APPENDIX D. SLAVE EXPORTS AND COAST DISTANCE

FIGURE A.1. Slave exports and distance from coast

Notes: This figure plots the cumulate percentage of all exportsin the Indian Ocean and Atlantic slave trades, reported in Nunnand Wantchekon (2011), against the distance of each ethnic groupcentroid from the coast.


APPENDIX E. DATA EXAMPLE

FIGURE A.2. Temperature deviations from mean: Benguela and Whydah

Notes: Temperature deviations from port means are in degreescelsius.


APPENDIX F. KERNEL DENSITIES

FIGURE A.3. Kernel densities of slave exports

FIGURE A.4. Kernel density of time to departure from Africa

(1)

Anomaly (Interpolated) -576.496***

Standard errors clustered by

Point X Year (171.082)

1° by 1° square (72 squares) (248.310)





Closest temperature point (28 points) (249.538)

CGM standard errors clustered by

Point X Year (171.082)






Closest temperature point (28 points) (250.454)

Closest temperature point and year (286.322)

Observations

Year FE Y

Port FE Y

Table A0. Alternative standard errors using a linear estimator

The dependent variable is slave exports. All regressions are OLS with port

and year fixed effects unless otherwise indicated.

Heterogeneity

Linear port trends -1,679.160** Including Temperature X Tsetse Suitability -4,420.368*** Known slaves + Region known -2,132.065***

(669.884) (1,631.667) (772.722)

Obs. 18,358 Coef. on Temp X Tsetse 1,750.484 Obs. 18,358

(1,986.938)

Quadratic region trends † -1,704.421*** Obs. 17,536 Slaves landed in New World -2,647.996***

(624.310) (999.666)

Obs. 18,358 Dropping US Civil War -3,032.921*** Obs. 18,358

(1,107.456)

Pre-1807 -2,145.928** Obs. 17,688 Closest temperature point -2,629.395***

(929.265) (790.065)

Obs. 10,318 Including interaction with El Nino Years -3,016.934*** Obs. 18,358

(1,097.403)

Post-1806 -2,191.968* Coef. on Temp X El Nino -27.898 Temperature: Ethnicities within 500 km -2,485.806***

(1,242.838) (47.893) (898.906)

Obs. 8,040 Obs. 18,358 Obs. 17,380

Active ports only -2,307.081*** Control for climate trend at NW port -2,635.310*** Slaves normalized by population density -760.331***

(776.294) (921.508) (271.219)

Obs. 6,780 Obs. 17,408 Obs. 18,358

Control for New World Temperature -3,090.331*** Including neighbor's anomaly † -10,441.490* No temperature points over water -3,005.996***

(1,116.947) (5,792.334) (629.668)

Obs. 18,358 Coef. on neighbor's anomaly 7,475.588 Obs. 18,358

(5,298.468)

Control for prices -2,235.841** Obs. 18,358 Log temperature on RHS -76,199.646***

(882.578) (25,762.309)

Obs. 10,854 Control for slave trading power's shock -3,084.699*** Obs. 18,358

(1,141.105)

Temperature shock at modal destination -3,315.021*** Obs. 18,358 Log (1+slave exports) on LHS -95.373***

(1,141.054) (27.881)

Obs. 17,536 De-meaned temperature if ≥ 0 -3,270.425** Obs. 18,358

(1,297.770) Outliers

Including Temperature X Bovines -4,189.436*** De-meaned temperature if < 0 -2,871.418** No high dfbeta -2,019.513***

(1,054.179) (1,268.771) (612.161)

Coef. on Temp X Bovines 2,644.148** Obs. 18,358 Obs. 17,816

(1,234.415)

Obs. 18,358 Measurement Top 50% of ports -3,288.917***

Known slaves -1,848.221*** (1,153.896)

Including Temperature X Husbandry -3,713.268* (696.018) Obs. 9,179

(1,900.544) Obs. 18,358

Coef. on Temp X Husbandry 394.664 Top 50% of years by port -1,885.934*

(796.812) (997.536)

Obs. 18,358 Obs. 9,246

Table A1: Robustness checks 1

Notes: ***Significant at 1%, **Significant at 5%, *Significant at 10%. Standard errors clustered by closest climate point in parentheses. The dependent variable is slave exports. All regressions are tobit with port

and year fixed effects unless otherwise indicated. † Anomaly used in place of temperature.

Level of observation

Artificial squares (1x1) -3,490.732** Port mean anomaly -2,397.368**

(1,395.269) (1,028.790)

Obs. 9,864 Obs. 18,358

Artificial squares (5x5) -5,524.108*** Include lag temperature -2,297.655***

(2,098.764) (790.881)

Obs. 3,836 Obs. 18,224

Region-level -11,394.596* Quadratic in year, rather than FE -1,206.751

(6,355.888) (795.406)

Obs. 1,096 Obs. 18,358

Murdock ethnicities -3,997.547*** Including lag slave exports

(1,339.742) Include lag slaves -1,858.215***

Obs. 7,672 (713.707)

Obs. 18,224

Estimation

OLS -576.496** Instrument for lag slaves with lag difference -1,933.028***

s.e. clustered by point (250.453) (726.435)

s.e. clustered by Conley's method (279.406) Obs. 18,090

Obs. 18,358

Port mean anomaly, year F.E., lag slave -1,340.205**

Dependent variable: Any slaves (OLS) -0.064* (603.091)

(0.034) Obs. 18,224

Obs. 18,358

OLS with lag -387.113**

No zeroes (OLS) -2,582.865** (176.219)

(953.700) Obs. 18,224

Obs. 3,206

Arellano-Bond ‡ -613.994*

Collapse to 5-year intervals -4,634.630*** (345.046)

(1,759.778) Obs. 18,090

Obs. 3,752

Table A2: Robustness checks 2

Notes: ***Significant at 1%, **Significant at 5%, *Significant at 10%. Standard errors clustered by closest climate point in parentheses. The

dependent variable is slave exports. All regressions are tobit with port and year fixed effects unless otherwise indicated. † Anomaly used in

place of temperature. ‡ Robust, rather than clustered, standard errors reported.

(1) (2) (3) (4)

Desert Dry Savannah Sub-humid Moist Savannah

Dry Savannah 0.94

Sub-humid 0.14 0.30

Moist Savannah 0.02 0.00 0.35

Humid forest 0.00 0.00 0.02 0.04

Senegambia Sierra Leone Windward Gold Coast

Sierra Leone 0.03

Windward 0.01 0.09

Gold Coast 0.59 0.14 0.04

Benin 0.28 0.02 0.01 0.06

Biafra 0.09 0.99 0.24 0.05

West-Central 0.01 0.00 0.00 0.00

Southeast 0.05 0.01 0.00 0.02

Benin Biafra West-Central

Biafra 0.02

West-Central 0.49 0.00

Southeast 0.19 0.00 0.40

p-values: Results by agro-ecological zone

Notes: ***Significant at 1%, **Significant at 5%, *Significant at 10%. These p-values test the equality of the

coefficients reported in Table 3 in the text.

p-values: Results by region

Table A3: Tests of coefficient equality in Table 3

(1)

Temperature at point j X point is within:

200 km to 300 km 0.654***

(0.011)

300 km to 400 km 0.553***

(0.004)

400 km to 500 km 0.486***

(0.005)

500 km to 600 km 0.513***

(0.015)

600 km to 700 km 0.384***

(0.005)

700 km to 800 km 0.334***

(0.004)

800 km to 900 km 0.388***

(0.010)

900 km to 1000 km 0.285***

(0.005)

1000 km to 1100 km 0.201***

(0.005)

1100 km to 1200 km 0.213***

(0.004)

1200 km to 1300 km 0.201***

(0.007)

1300 km to 1400 km 0.122***

(0.003)

1400 km to 1500 km 0.095***

(0.004)

1500 km to 1600 km 0.170***

(0.007)

1600 km to 1700 km 0.056***

(0.003)

1700 km to 1800 km 0.048***

(0.005)

1800 km to 1900 km 0.073***

(0.005)

1900 km to 2000 km 0.055***

(0.004)

Pair (i,j) FE Y

Year FE Y

Observations 2,332,440

Table A4: Temperature correlations over space: 1980-2000

Notes: ***Significant at 1%, **Significant at 5%, *Significant at 10%.

Robust standard errors in parentheses. The dependent variable is

temperature. The estimator is OLS.

(1) (2) (3) (4)

(Year-1500) X (1500 to 1550) -0.008*** -0.005***

(0.000) (0.000)

(Year-1550) X (1550 to 1600) 0.002*** -0.003***

(0.001) (0.000)

(Year-1600) X (1600 to 1650) 0.000 0.001***

(0.001) (0.001)

(Year-1650) X (1650 to 1700) 0.003*** 0.002***

(0.000) (0.000)

(Year-1700) X (1700 to 1750) 0.003*** -0.000

(0.000) (0.000)

(Year-1750) X (1750 to 1800) -0.003*** -0.002***

(0.000) (0.000)

(Year-1800) X (1800 to 1850) 0.003*** -0.003***

(0.000) (0.000)

(Year-1850) X (1850 to 1900) 0.039*** -0.015***

(0.002) (0.001)

Registered: France -0.125*** -0.062***

(0.008) (0.005)

Registered: Portugal -0.081*** 0.059***

(0.008) (0.007)

Registered: Other 0.152*** 0.146***

(0.006) (0.005)

Region: Senegambia -0.115*** -0.148***

(0.015) (0.016)

Region: Windward -0.155*** -0.163***

(0.016) (0.016)

Region: Sierra Leone -0.158*** -0.205***

(0.015) (0.015)

Region: Gold Coast 0.112*** 0.054***

(0.015) (0.016)

Region: Bight of Benin -0.067*** -0.072***

(0.015) (0.015)

Region: Bight of Biafra -0.033** -0.089***

(0.014) (0.015)

Region: West-Central Africa -0.107*** -0.162***

(0.017) (0.018)

Region: Missing 0.775*** 0.733***

(0.014) (0.014)

Region: Other 0.368*** 0.309***

(0.019) (0.020)

Observations 34,948 34,948 34,948 34,948

Table A5: Predictors of missing data

Notes: ***Significant at 1%, **Significant at 5%, *Significant at 10%. Robust standard errors in

parentheses. The dependent variable is a dummy for whether the major port of slave

purchase is missing. All regressions are OLS.

(1) (2) (3) (4) (5) (6) (7) (8)

Anomaly 0.794* 0.721** 0.044** 0.051 -0.066 0.044* 0.134** 0.048

(0.462) (0.337) (0.018) (0.045) (0.045) (0.021) (0.050) (0.034)



Obs. 120 120 43 47 60 60 61 59

R2 0.137 0.495 0.749 0.558 0.597 0.610 0.541 0.792

(9) (10) (11) (12) (13) (14) (15)

Anomaly 0.040 -0.007 0.031 0.023 0.070*** 0.019 0.046***

(0.032) (0.023) (0.018) (0.021) (0.020) (0.031) (0.014)

Controls N Y Y Y Y Y Y

Time Period 1790s 1800s 1810s 1820s 1830s 1840s 1850s

Obs. 60 56 52 56 46 37 26

R2 0.638 0.462 0.477 0.465 0.554 0.458 0.884

(1) (2) (3) (4) (5) (6) (7) (8)

Anomaly 0.790* 0.726** 0.070* 0.053* 0.155** 0.062 0.178** 0.054*

(0.453) (0.345) (0.035) (0.030) (0.069) (0.042) (0.068) (0.027)



Obs. 120 120 134 134 134 134 134 134

R2 0.137 0.495 0.490 0.485 0.494 0.470 0.509 0.488

(9) (10) (11) (12) (13) (14) (15) (16)

Anomaly 0.039 0.053 0.021 0.039 0.047 0.033 0.037 0.050

(0.027) (0.034) (0.034) (0.044) (0.038) (0.027) (0.029) (0.041)



Obs. 134 134 134 134 134 134 134 134

R2 0.470 0.479 0.448 0.453 0.463 0.461 0.466 0.463

Panel B: Include "capital city" dummy

Table A6. Robustness checks for modern outcomes

Notes: ***Significant at 1%, **Significant at 5%, *Significant at 10%. Standard errors in parentheses clustered by closest

climate point. All regressions are OLS. The dependent variable is log light density. All regressions include a constant.

Controls are absolute latitude, longitude, the number of raster light density points within 500km of the port, dummies for

AEZs, distance from the nearest Atlantic or Indian Ocean port of slave demand, and average temperature over the

period 1902-1980.

Panel A: Discard inactive ports

(1) (2) (3) (4) (5) (6) (7) (8)

Anomaly 0.000*** -0.000* -0.059** -0.039* -0.070** -0.040 -0.072* -0.057**

(0.000) (0.000) (0.026) (0.020) (0.030) (0.026) (0.035) (0.027)


Time Period Total Total 1730s 1740s 1750s 1760s 1770s 1780s

Obs. 112 112 93 93 93 93 93 93

R2 0.233 0.830 0.864 0.852 0.867 0.833 0.836 0.851

(9) (10) (11) (12) (13) (14) (15) (16)

Anomaly -0.018 -0.001 -0.018 -0.030* -0.030 -0.028* -0.019 -0.017

(0.011) (0.010) (0.011) (0.017) (0.019) (0.016) (0.012) (0.011)



Obs. 93 93 93 93 93 93 93 93

R2 0.245 0.836 0.864 0.852 0.867 0.833 0.836 0.851

(1) (2) (3) (4) (5) (6) (7) (8)

Anomaly 0.794* 0.908*** 0.079** 0.060* 0.227*** 0.078* 0.184** 0.062**

(0.462) (0.304) (0.038) (0.032) (0.081) (0.045) (0.076) (0.030)


Time Period All All 1730s 1740s 1750s 1760s 1770s 1780s

Obs. 120 120 134 134 134 134 134 134

R2 0.137 0.703 0.630 0.623 0.651 0.619 0.644 0.632

(9) (10) (11) (12) (13) (14) (15) (16)

Anomaly 0.046 0.061 0.040 0.071 0.070 0.047 0.048 0.065

(0.029) (0.037) (0.036) (0.049) (0.043) (0.030) (0.032) (0.045)



Obs. 93 93 93 93 93 93 93 93

R2 0.824 0.817 0.827 0.839 0.834 0.840 0.824 0.822

Table A7. Additional robustness checks for modern outcomes

Panel A: Points not within 500 km of ports

Panel B: Additional controls

Notes: ***Significant at 1%, **Significant at 5%, *Significant at 10%. Standard errors in parentheses clustered by closest

climate point. All regressions are OLS. The dependent variable is log light density. All regressions include a constant.

Controls are absolute latitude, longitude, the number of raster light density points within 500km of the port, dummies for

AEZs, distance from the nearest Atlantic or Indian Ocean port of slave demand, and average temperature over the

period 1902-1980. Additional controls are distance from the national capital, distance from the nearest foreign border,

petroleum, and malaria suitability.

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